Increased adoption of best practices in ecological forecasting enables comparisons of forecastability

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dc.contributor.authorLewis, Abigail S. L.en
dc.contributor.authorWoelmer, Whitney M.en
dc.contributor.authorWander, Heather L.en
dc.contributor.authorHoward, Dexter W.en
dc.contributor.authorSmith, John W.en
dc.contributor.authorMcClure, Ryan P.en
dc.contributor.authorLofton, Mary E.en
dc.contributor.authorHammond, Nicholas W.en
dc.contributor.authorCorrigan, Rachel S.en
dc.contributor.authorThomas, R. Quinnen
dc.contributor.authorCarey, Cayelan C.en
dc.date.accessioned2022-06-17T19:33:22Zen
dc.date.available2022-06-17T19:33:22Zen
dc.date.issued2021-12-14en
dc.date.updated2022-06-17T19:26:33Zen
dc.description.abstractNear-term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross-ecosystem analysis of near-term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near-term (≤10-yr forecast horizon) ecological forecasting papers to understand the development and current state of near-term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near-term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near-term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1–7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables.en
dc.description.versionPublished versionen
dc.format.extent16 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifierARTN e02500 (Article number)en
dc.identifier.doihttps://doi.org/10.1002/eap.2500en
dc.identifier.eissn1939-5582en
dc.identifier.issn1051-0761en
dc.identifier.issue2en
dc.identifier.orcidThomas, Robert [0000-0003-1282-7825]en
dc.identifier.orcidCarey, Cayelan [0000-0001-8835-4476]en
dc.identifier.pmid34800082en
dc.identifier.urihttp://hdl.handle.net/10919/110832en
dc.identifier.volume32en
dc.language.isoenen
dc.publisherWileyen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000730058500001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsCreative Commons Attribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.subjectdata assimilationen
dc.subjectdecision supporten
dc.subjectecological predictabilityen
dc.subjectforecast automationen
dc.subjectforecast evaluationen
dc.subjectforecast horizonen
dc.subjectforecast uncertaintyen
dc.subjectiterative forecastingen
dc.subjectnear-term forecasten
dc.subjectnull modelen
dc.subjectopen scienceen
dc.subjectuncertainty partitioningen
dc.subjectOPPORTUNITIESen
dc.subjectUNCERTAINTYen
dc.subjectINFORMATIONen
dc.subjectPOPULATIONen
dc.subjectPREDICTIONen
dc.subjectMODELen
dc.subject.meshPhytoplanktonen
dc.subject.meshPollenen
dc.subject.meshChlorophyllen
dc.subject.meshUncertaintyen
dc.subject.meshEcosystemen
dc.subject.meshPlant Transpirationen
dc.subject.meshForecastingen
dc.titleIncreased adoption of best practices in ecological forecasting enables comparisons of forecastabilityen
dc.title.serialEcological Applicationsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
dcterms.dateAccepted2021-10-05en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Natural Resources & Environmenten
pubs.organisational-group/Virginia Tech/Natural Resources & Environment/Forest Resources and Environmental Conservationen
pubs.organisational-group/Virginia Tech/Scienceen
pubs.organisational-group/Virginia Tech/Science/Biological Sciencesen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Natural Resources & Environment/CNRE T&R Facultyen
pubs.organisational-group/Virginia Tech/Science/COS T&R Facultyen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciences/Durelle Scotten

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